Search Results (275)

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

With the integration of numerous distributed energy resources (DERs) and buildings with diverse energy demands, the inherent vulnerability of agricultural microgrids poses escalating security threats. Harnessing the regulatory capabilities of diverse building loads and energy storage systems to mitigate voltage excursions caused by DER generation in microgrids is of significant importance. Therefore, a data mechanism fusion-driven microgrid planning method is proposed in this paper, aiming to enhance the security of microgrids and optimize the utilization of DERs. A comprehensive agricultural microgrid model that incorporates intricate constraints of various types of buildings is established, including greenhouses, refrigeration houses and residences. Based on this model, a site selection and capacity determination planning methodology is proposed, taking into account wind turbines (WTs), photovoltaics (PVs), electric boilers (EBs), battery energy storage systems (BESSs), and heat storage devices. To address the limitations of traditional greenhouse models in accurately predicting indoor temperatures, a temperature field prediction method for greenhouses is proposed by leveraging a generalized regression neural network (GRNN) to train and modify the model indicators. Case studies based on a modified IEEE 33-bus system verified the effectiveness and rationality of the proposed method. Full article

The growing global concern for sustainability and energy conservation has led to the adoption of energy management systems to minimise the impacts of energy intensive processes. This study reviews the evolution, the applications, and implementation techniques of energy management systems with an emphasis on harbour operations. Through the mapping of the research on energy management systems post-1973, the literature review demonstrated a substantial transformation of the systems from basic monitoring in the building sector to complex artificial intelligence analyses in smart and microgrids, industries, renewable energy sources integration, transportation, and harbours. Initial broad search (1973–2025) identified 22,003 EMS-related records; targeted port–EMS queries yielded 214 records, of which 139 unique records remained after de-duplication and 78 full texts were assessed. Finally, 27 studies were included in the quantitative synthesis. A meta-analysis in conjunction with an article review, and a weighted sum model coupled with sensitivity analyses revealed promising results for harbour energy management system implementation in terms of peak/load shifting, on-shore power supply, and real-time energy monitoring. The findings showed that energy management system efficacy is linked to maturity levels and strategic deployment of the measures/policies in each stage. Full article

This study investigates the integration of green hydrogen into building energy systems using local solar power, with the electricity grid serving as a backup plan. A comprehensive bottom-up analysis compares six energy system configurations: the natural gas grid boiler system, all-electric heat pump system, natural gas and hydrogen blended system, hydrogen microgrid boiler system, cogeneration hydrogen fuel cell system, and hybrid hydrogen heat pump system. Energy efficiency evaluations were conducted for 25 homes within one block in a neighborhood across five typological house stocks located in Stoke-on-Trent, UK. This research was modeled using a spreadsheet-based approach. The results highlight that while the all-electric heat pump system still demonstrates the highest energy efficiency with the lowest consumption, the hybrid hydrogen heat pump system emerges as the most efficient hydrogen-based solution. Further optimization, through the implementation of a peak-shaving strategy, shows promise in enhancing system performance. In this approach, hybrid hydrogen serves as a heating source during peak demand hours (evenings and cold seasons), complemented by a solar energy powered heat pump during summer and daytime. An hourly operational configuration is recommended to ensure consistent performance and sustainability. This study focuses on energy performance, excluding cost-effectiveness analysis. Therefore, the cost of the energy is not taken into consideration, requiring further development for future research in these areas. Full article

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions. Full article

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions. Full article

Microgrids and load shifting can improve resilience and lower costs for electricity customers. The costs to deploy each have decreased and helped accelerate their deployment in the U.S. and globally. However, previous research has focused minimally on the combined benefit or “stacked economic value” that these assets could provide jointly. This article evaluates the financial value when those assets are combined and optimized jointly. The methods are demonstrated for a U.S. government facility with an existing microgrid and building automation system, with optimizations that vary the percentage load shifted and the duration of time the load can be shifted. The economic benefits of load shifting are greater when combined with a microgrid and coordinated dispatch of loads and microgrid assets. The methods and case study results illustrate “stacked economic value” showing energy charge reductions are 56–252% greater and demand charge reductions are 96–226% greater when load shifting is combined with a microgrid as compared to load shifting without a microgrid. Increasing the amount and duration of load shifting improves the stacked economic value as more loads are scheduled coincident with on-site generation to offset or completely avoid utility purchases during peak pricing periods, an underlying behavior that enables stacked economic value and increased financial savings. The percentage reduction in demand charges is greater than energy charges—a generalizable finding—but the relative impact on utility expenditures is dependent on the utility tariff structure and composition of demand charges and energy charges in the utility bill. In this case study, demand charge reductions were four times greater than energy charge reductions, but the financial savings of demand charges are less due to their smaller proportion of utility charges. This suggests that the stacked economic value of microgrids and load control may be even more significant in locations with electricity tariffs that more heavily weight billing towards demand charges than energy charges. Full article

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security. Full article

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric cars. In particular, this paper describes the coordination between a battery management algorithm that optimally assigns its capacity so that at least a part is reserved for mobility and a vehicle-to-building (V2B) service algorithm that uses a share of EV battery energy to improve user participation in renewable energy exploitation at home and at work. The system offers the user the choice of always maintaining a minimum charge for mobility or providing more flexible use of energy for business needs while maintaining established vehicle autonomy. Suitable management at home and at work allows always charging the vehicle to the required level of charge with renewable power excess, highlighting how the cooperation of home and work charging may provide novel frameworks for a smarter and more sustainable integration of electric mobility, reducing energy consumption and providing more effective energy management. The effectiveness of the proposed solution is demonstrated in a realistic configuration with real data and an experimental setup. Full article

Efficient scheduling of Energy Storage Systems (ESS) within microgrids has emerged as a critical issue to ensure energy cost reduction, peak shaving, and battery health management. For ESS scheduling, both single-agent and multi-agent deep reinforcement learning (DRL) approaches have been explored. However, the former has suffered from scalability to include multiple objectives while the latter lacks comprehensive consideration of diverse user objectives. To defeat the above issues, in this paper, we propose a new DRL-based scheduling algorithm using a multi-agent proximal policy optimization (MAPPO) framework that is combined with Pareto optimization. The proposed model employs two independent agents: one is to minimize electricity costs and the other does charge/discharge switching frequency to account for battery degradation. The candidate actions generated by the agents are evaluated through Pareto dominance, and the final action is selected via scalarization-reflecting operator-defined preferences. The simulation experiments were conducted using real industrial building load and photovoltaic (PV) generation data under realistic South Korean electricity tariff structures. The comparative evaluations against baseline DRL algorithms (TD3, SAC, PPO) demonstrate that the proposed MAPPO method significantly reduces electricity costs while minimizing battery-switching events. Furthermore, the results highlight that the proposed method achieves a balanced improvement in both economic efficiency and battery longevity, making it highly applicable to real-world dynamic microgrid environments. Specifically, the proposed MAPPO-based scheduling achieved a total electricity cost reduction of 14.68% compared to the No-ESS case and achieved 3.56% greater cost savings than other baseline reinforcement learning algorithms. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

A paradigm transition from centralized to decentralized energy systems has occurred, which has increased the deployment of renewable energy sources (RESs) in renewable energy communities (RECs), promoting energy independence, strengthening local resilience, increasing self-sufficiency, and moving toward CO₂ emission reduction. However, the erratic and unpredictable generation of RESs like wind, solar, and other sources make these systems necessary, and a lot of interest in energy storage systems is increasing because they have rapidly become the cornerstone of modern energy infrastructure, and there is a trend towards using more RESs and decentralization, resulting in increased self-sufficiency. Additionally, ESS is increasingly being installed at or close to the point of energy generation and consumption, like within residences, buildings, or community microgrids, instead of at centralized utility-scale facilities, referred to be decentralized. By storing and using energy in the same location, this localized deployment reduces transmission losses, facilitates quicker response to changes in demand, and promotes local autonomy in energy management. Since the production of renewable energy is naturally spread, decentralizing storage is crucial to optimizing efficiency and dependability. This article also focuses on energy storage systems, highlighting the role and scope of ESSs along with the services of ESSs in different parts of the power system network, particularly in renewable energy communities (RECs). The classification of various ESS technologies and their key features, limitations, and applications is discussed following the current technological and significant information trends and discussing the ESS types for the RECs with different options as per the capacity, like small, medium, and large scale. It covers the overall scenario and progress, like overall European ESS installed capacity, and the work relevant to ESSs in RECs with different aspects, following the literature review. Additionally, it draws attention to the gaps and significant challenges related to ESS technologies and their deployment. Key future suggestions have also been given as per the current trends of technological information and significant information that may affect those trends globally in the future and would be helpful in the growth of ESSs integration in RECs. The authors also suggest the role of the government, stakeholders, and supportive policies that can aid in the implementation of ESS technologies in RECs. Full article